Long-term memory gated encoding in working memory

工作记忆中的长期记忆门控编码

• 批准号: EP/X030849/1
• 负责人: Eda Mizrak
• 金额: $ 26万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX030849%2F1
• 关键词: Long; term; memory; gated; encoding

Humans have the remarkable ability to hold onto information - preserving it in working memory (WM) where it is ready for ongoing thought and action. Although WM is undisputedly limited to storing only small amounts of information, it is clear that we can extend this capacity by capitalizing on existing knowledge in long-term memory (LTM). But how do we connect prior knowledge in LTM to benefit WM? Imagine you need to hold a phone number in mind: you recognize that it consists of a familiar chunk such as your birth year, and it immediately becomes easier to remember. Instead of encoding each digit of the phone number in WM, one can strategically encode only the novel parts in WM and recruit LTM for the known parts. Although this is a well-established behavioral finding, theories diverge in the mechanism of connection between LTM and WM, and neural theories have never been tested empirically. I propose a novel, neurally plausible mechanism in which encoding in WM is guided by LTM to proactively decrease the cognitive load on the system: "LTM-gated encoding". Objective 1 will elucidate the bounds of this mechanism in a novel behavioral task, Objective 2 will delineate the neural interactions underlying this mechanism with magnetoencephalography (MEG), andObjective 3 will describe the mechanism with a computational model. I will receive training in state-of-the-art neurophysiological and machine-learning methods which will prepare me for a career in computational neuroscience. The project will integrate the supervisor's technical and computational skills with my empirical domain knowledge, to build and test this new quantitative framework which contrasts with previous work in the field.

人类拥有保存信息的非凡能力——将其保存在工作记忆（WM）中，为持续的思考和行动做好准备。尽管长期记忆毫无疑问仅限于存储少量的信息，但很明显，我们可以通过利用长期记忆（LTM）中的现有知识来扩展这种能力。但是我们如何将LTM中的先验知识与WM联系起来呢？想象一下，你需要记住一个电话号码：你意识到它是由一个熟悉的块组成的，比如你的出生年份，它立即变得更容易记住。代替在WM中编码电话号码的每个数字，可以策略性地只编码WM中的新部分，并为已知部分招募LTM。虽然这是一个成熟的行为学发现，但在LTM和WM之间的联系机制上，理论存在分歧，神经理论从未得到实证检验。我提出了一种新颖的、神经上合理的机制，在这种机制中，WM中的编码由LTM引导，以主动减少系统的认知负荷：“LTM门控编码”。目的1将在一个新的行为任务中阐明这一机制的界限，目的2将用脑磁图（MEG）描述这一机制背后的神经相互作用，目的3将用计算模型描述这一机制。我将接受最先进的神经生理学和机器学习方法的培训，这将为我在计算神经科学领域的职业生涯做好准备。该项目将把导师的技术和计算技能与我的经验领域知识结合起来，建立和测试这个新的定量框架，与该领域以前的工作形成对比。

项目成果

Syntax through rapid synaptic changes

• DOI: 10.1101/2023.12.21.572018
• 发表时间: 2023-12
• 期刊: bioRxiv
• 作者: Lin Sun;Sanjay G. Manohar

Neural signature of flexible coding in prefrontal cortex.

• DOI: 10.1073/pnas.2200400119
• 发表时间: 2022-10-04
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Dynamic in-flight shifts of working memory resources across saccades.

• DOI: 10.1037/xhp0000960
• 发表时间: 2022-01
• 期刊: Journal of experimental psychology. Human perception and performance
• 作者: Udale R;Tran MT;Manohar S;Husain M
• 通讯作者: Husain M

Rapid vigilance and episodic memory decrements in COVID-19 survivors.

• DOI: 10.1093/braincomms/fcab295
• 发表时间: 2022
• 期刊: Brain communications
• 影响因子: 4.8
• 作者: Zhao S;Shibata K;Hellyer PJ;Trender W;Manohar S;Hampshire A;Husain M
• 通讯作者: Husain M

Vividness of visual imagery questionnaire scores and their relationship to visual short-term memory performance.

• DOI: 10.1016/j.cortex.2021.10.011
• 发表时间: 2022-01
• 期刊: Cortex; a journal devoted to the study of the nervous system and behavior
• 作者: Tabi YA;Maio MR;Attaallah B;Dickson S;Drew D;Idris MI;Kienast A;Klar V;Nobis L;Plant O;Saleh Y;Sandhu TR;Slavkova E;Toniolo S;Zokaei N;Manohar SG;Husain M
• 通讯作者: Husain M